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Google Professional-Data-Engineer Certification Exam consists of multiple-choice questions and performance-based tasks that simulate real-world scenarios. Professional-Data-Engineer exam is divided into four main domains: Designing data processing systems, building and operationalizing data processing systems, integrating data sources, and managing data processing infrastructure.

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The Google Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions, and candidates are given two hours to complete it. Professional-Data-Engineer exam covers a range of topics, including designing data processing systems, building and maintaining data structures and databases, data analysis, machine learning, and data visualization.

Google Certified Professional Data Engineer Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

A. Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
B. Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.
C. Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
D. Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Answer: D

Explanation:
BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:
* 1: Introduction to BigLake tables
* 2: Deep dive on how BigLake accelerates query performance
* 3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)

NEW QUESTION # 300
Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

A. Cloud Composer
B. Cloud Dataflow
C. Cloud Dataprep
D. Cloud Dataproc

Answer: A

Explanation:
Cloud Composer uses airflow which is open source and can help to orchestrate jobs.

NEW QUESTION # 301
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

A. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
B. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
C. Check the dashboard application to see if it is not displaying correctly.
D. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

Answer: A

Explanation:
Explanation:

NEW QUESTION # 302
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
E. Use Cloud Deployment Manager to automate access provision.

Answer: C,E

Explanation:
Explanation

NEW QUESTION # 303
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

A. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
B. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
C. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION # 304
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